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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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Reducing energy consumption in the IoT systems with unlimited number of devices

Ch. 9470596. P. 1–6.
Borisovskaya A. V., A. M. Turlikov

This paper considers a model of the Internet of Things (IoT) system with a potentially unlimited number of devices. In such systems, a large number of conflicts arises that leads to retransmissions of messages. A large number of retransmissions causes transmitter operation time growth. More transmitting time in turn increases energy consumption and reduce device battery life. For considered model a method is proposed. The method allows to reduce the average number of retransmissions for the given intensity of the input stream. As a consequence, energy consumption can be reduced. This method combines numerical computation and simulation

Language: English
DOI
Keywords: simulationStability analysisLoRaWAN Internet of Things energy consumptionAdaptive Synchronous ALOHARandom Multiple Access

In book

WAVE ELECTRONICS AND ITS APPLICATION IN INFORMATION AND TELECOMMUNICATION SYSTEMS. 2021. (WECONF 2021) St. Petersburg, Russia, 31 May - 4 June 2021
IEEE, 2021.
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